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Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space

Neural Network research from Evolving Artificial Intelligence Lab produces interesting generation of subjects, especially as they appear to morph from one to another:

Generating high-resolution, photo-realistic images has been a long-standing goal in machine learning. Recently, Nguyen et al. (2016) showed one interesting way to synthesize novel images by performing gradient ascent in the latent space of a generator network to maximize the activations of one or multiple neurons in a separate classifier network. In this paper we extend this method by introducing an additional prior on the latent code, improving both sample quality and sample diversity, leading to a state-of-the-art generative model that produces high quality images at higher resolutions (227x227) than previous generative models, and does so for all 1000 ImageNet categories. 

Your body is an incredibly bizarre machine.